Color medical imaging on film

ABSTRACT

A method of printing a high density image. An image is accessed, wherein the image has a plurality of pixels, and each pixel has an r,g,b value. For each pixel, each r,g,b value is separated into separate grayscale components and color components. A lookup table is applied to each grayscale and color component to generate an ink value for each of the print heads. The ink values are combined to produce a final ink output value for each pixel, and the final ink output values are employed for printing on medium using an ink-jet printer.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Ser. No. 61/562,075 entitled “COLOR MEDICAL IMAGING ON FILM”, filed on Nov. 21, 2011 to Szafraniec, which was a Continuation-in-Part of U.S. Ser. No. 13/276,401 entitled “HIGH DENSITY INK-JET PRINTING ON A TRANSPARENCY IN A CIELAB COLORSPACE”, filed on Oct. 19, 2011, to Szafraniec, which was itself a Continuation-in-Part of U.S. Ser. No. 13/117,444 entitled “HIGH DENSITY INK-JET PRINTING ON A TRANSPARENCY,” filed on May 27, 2011 to Szafraniec, which claimed priority to U.S. Ser. No. 61/416,558 entitled “HIGH DENSITY INK-JET PRINTING ON TRANSPARENCY”, filed on Nov. 23, 2010 to Szafraniec. All of the above are hereby incorporated by reference herein in their entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The following notice applies to the software and data as described below and in the drawings that form a part of this document: Copyright Carestream, Inc., All Rights Reserved.

FIELD OF THE INVENTION

This invention relates generally to the field of ink-jet printing, and in particular to high density color ink-jet printing on a transparent medium.

BACKGROUND

In a typical ink-jet printing system, ink droplets are ejected from a nozzle at high speed towards a recording medium (e.g., film) to produce an image on the recording medium. An image processing system can convert pixel values of an image into an ink output value for a printer. The ink output values comprise the raw values used by the printer as a command corresponding to the quantity of ink droplets to eject at a given point on the recording medium. Ideally, the printer will output a quantity of ink in the area on the recording medium corresponding to the pixel such that that area appears to an observer to have the color, tone, tint, shade, and optical density of the pixel in the image.

A given pixel value can correspond to multiple ink output values. For example, a pixel of a given color, tone, tint, shade, and optical density can be reproduced by the printer using a combination of multiple colors of ink. A typical ink-jet printer uses black, cyan, magenta, and yellow, although other and/or additional colors can be used. Accordingly, to reproduce a given pixel, the printer can output some portion of one or more of the ink colors available.

A transfer function can be used by the image processing system to convert the pixel value to the corresponding ink output values for each ink nozzle (e.g., each different color) on the printer. In some examples, the transfer function can take the form of a look up table (LUT) relating pixel values to the ink output values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic of a printer according to one example of the present disclosure.

FIG. 2 shows another schematic of a image processing method according to another example of the present disclosure.

FIG. 3 shows a plot of optical density versus pixel value according to one example of the present disclosure.

FIG. 4 shows a plot of optical density versus pixel value according to one example of the present disclosure.

FIG. 5 shows a plot of K,C,M,Y values versus shading according to one example of the present disclosure.

FIG. 6 shows a plot of optical density versus pixel value according to one example of the present disclosure showing the total optical density curve vs. the component optical densities.

FIG. 7 shows a plot of optical density versus pixel value according to one example of the present disclosure showing the look up table vs. the theoretical optical density.

FIG. 8 shows a plot of optical density versus pixel value according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for black.

FIG. 9 shows a plot of optical density versus pixel value according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for cyan.

FIG. 10 shows a plot of optical density versus pixel value according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for magenta.

FIG. 11 shows a plot of optical density versus pixel value according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for yellow.

FIG. 12 shows a schematic of an example machine according to one example.

FIG. 13 shows a plot of optical density versus pixel value (bit value) according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for example 1.

FIG. 14 shows a plot of optical density versus pixel value (bit value) according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for example 2.

FIG. 15 shows a plot of optical density versus pixel value (bit value) according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for example 3.

FIG. 16 shows a plot of a* vs. pixel value (bit value) in the three examples, according to an example.

FIG. 17 shows a plot of b* vs. pixel value (bit value) in the three examples, according to an example.

FIG. 18 shows a plot of optical density vs. pixel value comparing the density model and the observed optical densities for light black.

FIG. 19 shows a plot of optical density vs. pixel value comparing the density model and the observed optical densities for light cyan.

FIG. 20 shows a plot of optical density vs. pixel value comparing the density model and the observed optical densities for light magenta.

FIG. 21 shows a plot of optical density vs. pixel value comparing the density model and the observed optical densities for light light black.

FIG. 22 shows a plot of output pixel values (BVx) for a given input pixel value (BV) according to one example of the present disclosure.

FIG. 23 shows a plot of optical density versus pixel value (bit value) according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for example 4.

FIG. 24 shows a plot of output pixel values (BVx) for a given input pixel value (BV) according to one example of the present disclosure.

FIG. 25 shows a plot of optical density versus pixel value (bit value) according to one example of the present disclosure showing the predicted optical density vs. the measured optical density for example 5.

FIG. 26 shows in one example a plot of a* vs. pixel value (bit value) for both example 4 (bottom line) and example 5 (top line)

FIG. 27 shows in one example, a plot of b* vs. pixel value (bit value) for both example 4 (top line) and example 5 (bottom line).

FIG. 28 shows an example method for printing a color image according to some examples of the present disclosure.

FIG. 29 shows an example of the results of printing a color image according to some examples of the present disclosure.

In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments discussed in the present document.

DETAILED DESCRIPTION

Disclosed is a system and method for printing high quality color images on a transparent medium with appropriate tint and tone. The resulting image achieves near-linear perceived brightness in relation to image pixel value with high optical densities. This perceived brightness is the result of a look-up table or transfer function that correctly maps input image pixel values to ink output values.

FIG. 1 illustrates one example of a system 100 for printing on a transparent medium. The system 100 can include an ink-jet printer 102 and an image processing module 104 communicatively coupled to the ink-jet printer 102. The image processing module 104 can be configured to convert pixel values from an image into ink output values for the printer 102. The ink-jet printer 102 can include a plurality of ink nozzles (106-112), each nozzle configured to output droplets of ink onto a recording medium (e.g., paper, transparency) 114. In an example, the printer 102 can include a first nozzle configured to output a black (K) ink 106, a second nozzle configured to output a cyan (C) ink 108, a third nozzle configured to output a magenta (M) ink 110, and a fourth nozzle configured to output a yellow (Y) ink 112. In other examples, the printer 102 can include additional nozzles for, for example, light black and dark black, light cyan and dark cyan, light magenta and dark magenta, and light yellow and dark yellow. In yet other examples, the printer 102 can include other nozzles and other colors.

The image processing module 104 can include a processor 116 and a memory device 118. In some examples, the image processing module 104 can be physically integral with the printer 102. In other examples, the image processing module can be implemented by another computing device (e.g., a general purpose computer) that is communicatively coupled to the printer 102. In yet other examples, the image processing module 104 can be partially implemented on another computing device and partially implemented on the printer 102. In any case, however, the image processing module can be communicatively coupled to the printer 102 through data interface 120 such that the ink output values from the image processing module 104 can be used by the printer 102 to command the nozzles to output a given quantity of ink at a given location. The data interface can be any method known to those skilled in the art including, but not limited to, a physical cable (e.g., a universal serial bus (USB) cable, serial cable, parallel or LPT cable) and a wireless medium (e.g., a Bluetooth, Wi-Fi, or cellular protocol).

Attached to or in proximity to the ink-jet printer 102 may be a mechanism 122 for supporting and making available media 114 to the ink-jet printer 102. Such mechanisms may comprise, for example, trays, cassettes, feed slots, roller systems and the like.

The feed mechanism 122, data interface 120, and ink cartridges and nozzles 106-112 are communicatively coupled to control unit 124. Control unit has a processor 126 and memory 128 for receiving print jobs, and controlling media support feed mechanism 122, data interface 120 and ink cartridges w/nozzles 106-112 to produce printed image 130. In some examples, processor 116 and processor 126 can be the same or different processors. In some examples, memory 128 can be the same or different memory as memory 118.

In proximity to the ink-jet printer 102 may be a densitometer 132 that may be capable of reading transmittance and reflective densities of the printed image 130. In some embodiments, the densitometer 132 may be positioned at a distance from the printer 102. In other embodiments, the densitometer 132 may be incorporated into the printer 102 or, in some location in between. The densitometry data 134 may be transmitted to the Image Processing module 104 using a variety of known methods, such as, for example, standard or proprietary wired interfaces, or wireless connections, physical portable data devices such as CD, flash memory, manual input and the like. Additionally, the densitometry data 134 may optionally be transmitted through, for example, a local network, or optionally through one or more wide area networks and the like.

FIG. 2 illustrates a block diagram of an example method 200 where pixel values 136 are provided to the Image Processing module 104. The pixel values 136 may comprise digital image data. Such data may comprise data from an image data capture device or data from a variety of digital storage media. The pixel values 136 may comprise meta-data associated with the image. In some examples, pixel values 136 can be medical image data, including but not limited to x-ray images, magnetic resonance images (MRI), and computed tomography (CT) images. In examples where the image corresponding to the pixels includes a medical image, the pixel values may be communicated, for example, using the standard Digital Imaging and Communications in Medicine (DICOM) format. Other formats are possible, including JPEG, GIF, PNG, and the like. The densitometry data 134 is provided to the image processing module 104 where it might be used to comprise part of a model describing interactions between ink, output media and printer values.

The recording medium 114 may comprise an ink-jet recording medium (e.g., a film). An ink-jet recording medium may comprise at least one image-receiving layer, which receives the ink 142 from the ink-jet printer 102 during printing. The recording medium 114 may also comprise a substrate or support, which may be opaque or transparent. An opaque support is used in films that may be viewed using light reflected by a reflective backing, while a transparent support is used in films that may be viewed using light transmitted through the film. In some examples found throughout this specification, the recording medium 114 can be the one described in patent application Ser. No. 13/176,788 filed on Jul. 6, 2011 TRANSPARENT INK-JET RECORDING FILM by Simpson et al, commonly assigned, and incorporated herein by reference in its entirety.

The image processing module 104 can prepare an image for printing, for example, by resizing the image (either larger or smaller). A resized image will have a different number of pixels than the original. If larger, more than 1 pixel in the resized image may represent a single initial pixel. If smaller, one pixel in the resized image may represent multiple pixels in the original image.

The image may then go though Color Separation 138 to be converted to a series of color separation images representing the different inks available such that the combination would closely replicate the color and/or tone scale of the image. The choice and ratio of inks 142 used to closely replicate the desired color and tone of the image may comprise the use of an interaction model 144 to describe the interactions between the one or more ink(s) 142 and the recording medium 114. As ink formulations vary by manufacturer, and each recording medium also varies by type and manufacturer, how an ink will look on a particular recording medium will vary. The interaction model 144 attempts to compensate for the differing media and ink formulations to produce a consistent image. Additionally the transfer function 146 may comprise the output response of the ink-jet printer 102 to different input pixel values. The transfer function 146 converts pixel intensities into ink densities. That is, the transfer function 146 maps the desired colors into an amount of ink of each color to place on the recording medium 114.

The image may then be converted to a half-tone image in order to achieve the dynamic range (light to dark) desired, Half-toning 140 is a technique that simulates continuous tone imagery through the use of dots, varying either in size, in shape, or in spacing and involves converting each image pixel to a pattern of ink droplets. Separate CMYK color dots are laid down in a close pattern such that from a normal viewing distance, it appears to be a continuous shade. The pattern of the ink droplets may be such that the combined droplets can closely reproduce the different colors and tonal levels desired. The pattern for the drops may be chosen based on the drop size of the particular printer and the drops per inch (dpi) desired.

This data may then be sent to the ink-jet printer 102, which may proceed to eject the specified ink-droplets from a nozzle towards media 114 comprising a recording film, element, or medium to produce an output image 130. Additional image processing algorithms, which are known in the art, may optionally be applied throughout the image processing 130. Those skilled in the art will recognize that the equivalent of such image processing module 104 can be constructed in many different combinations of software and hardware.

Based on the type of recording medium and inks used, image quality is dependent on both the interaction model 144 and the transfer function 146 to produce the desired output. What constitutes an acceptable image quality varies from application to application. In some examples, the output image is to be used in medical applications. In some examples, this medical imagery will be used by radiologists or other medical professionals. In these examples, images produced on the recording medium 114 will be viewed the same way traditional film-based x-rays are viewed—by the use of back-lighting. In these examples, several factors are important: image quality (and in particular the absence of graininess), appropriate optical density, and appropriate tint and tone (so as to accurately reproduce color components).

Optical density (OD) is the visually perceivable and measurable absorption of light on the surface of a medium due to the presence of a colorant. Optical density is important because a particular density of black is required for decent viewing through a backlight. If the density is not high enough, the image may appear gray and will be generally unacceptable. Optical density is a function of both the amount of ink laid down by the printer, as well as the particular combination of colors laid down. Thus for example, an inkjet printer does not strictly use black ink to produce a black tone, it uses additional CMY (cyan, magenta, yellow) colors as well. So to produce a black with a desired optical density, a combination of different colors can be used. For x-rays, the maximum density may be between 2.0 and 4.0 to be effectively viewed. Additionally, the density should be perceived to be linear when viewed on a lightbox. If the density is not linearly bright, detail in the image is lost.

Graininess is the property of a printed image not appearing continuously smooth, or of appearing composed of grains. Graininess is the result of the printer not printing enough drops of ink per inch. Graininess and optical density are related in that a given color of ink or combination of colors may give a desired optical density but be too grainy. The optimum combination of ink to produce a desired optical density at a desired tone or shade also uses enough ink to lay down enough dots to make the image look continuous and not grainy. As mentioned before, it is desirable for the image density to be linear as perceived. This is not as easy as it may sound as the relationship between the amounts of the one or more inks deposited on the recording medium and optical density on the recording medium is not linear and may not be the same between different ink(s) and different media. The present disclosure, in one example, is a system and method for determining the amount of one or more inks to apply to a recording medium in order to generate a linear scale in perceived optical density between a minimum density (Dmin) and a maximum density (Dmax), where Dmax is sufficiently high for an intended use. Such a method is useful to reproduce an accurate image on a given recording medium with one or more inks.

The following grayscale information is provided for explanatory purposes and is described in U.S. Ser. No. 13/276,401 entitled “HIGH DENSITY INK-JET PRINTING ON A TRANSPARENCY IN A CIELAB COLORSPACE”, filed on Oct. 19, 2011, to Szafraniec, and U.S. Ser. No. 13/117,444 entitled “HIGH DENSITY INK-JET PRINTING ON A TRANSPARENCY,” filed on May 27, 2011 to Szafraniec, both of which are hereby incorporated by reference herein in their entirety.

Relationship Between Optical Density and Pixel Value for Grayscale Images

Optical density (OD) is related to transmittance according to the following equation: OD=−γ·Log₁₀ T.

Where T is the transmittance which is defined as the fraction of incident light at a specified wavelength that passes through a sample. Gamma, or γ, is used to quantify contrast and can be added to the standard OD formula as another parameter to increase equation fit with observed data. While the equation above uses transmittance T, a grayscale input image to the printer's transfer function utilizes values of between 0-255 representing the image brightness/darkness at a particular unit of the picture, called a pixel. In an example, for 256 bit levels (0-255) transmittance T can be expressed in terms of Pixel Value (or Bit Value) as shown in the following equation:

$T = \frac{255 - {PixelValue}}{255}$

In this example, transmittance is 1 (transparent) for a Pixel Value of 0, and transmittance is 0 (opaque) for a PixelValue of 255. A direct substitution of T into the Optical Density Equation would result in an Optical Density of 0 for a Pixel Value of 0 according to the following statement: PixelValue=0

T=1 T=1

OD=0

However, as mentioned above, the transmittance properties of most recording mediums include some minimum density (Dmin). Therefore, the relationship may be rewritten to comprise the following substitution: PixelValue=0

OD=D_(min)

With the changes above, the Optical Density may comprise the following relationship:

${OD} = {{{- \gamma} \cdot {{Log}_{10}\left( \frac{255 - {PixelValue}}{255} \right)}} + D_{m\; i\; n}}$

Additionally, most printed optical densities do not achieve complete opacity. Therefore, for most recording mediums there may be a maximum density (Dmax) that can be achieved as expressed: PixelValue=255

OD=D _(max)

Optical Density may be rewritten to account for Dmax as follows:

${OD} = {{{- \gamma} \cdot {{Log}_{10}\left\lbrack {1 + {\frac{PixelValue}{255} \cdot \left( {10^{- \frac{({D_{m\;{ax}} - D_{m\; i\; n}})}{\gamma}} - 1} \right)}} \right\rbrack}} + D_{m\; i\; n}}$ With the expression above you have the basic relationship between optical density (OD), Pixel Value, and gamma.

In an example, gamma, or contrast, from the above equation can be adjusted based on the recording medium. The selection of contrast, γ, comprises a significant influence on the final relationship between PixelValue, PV, and Optical Density.

For those occasions when γ>>1 (gamma is much greater than one) then:

${\frac{D_{{ma}\; x} - D_{m\; i\; n}}{\gamma}{\ln(10)}} \cong \frac{D_{m\;{ax}} - D_{m\; i\; n}}{\gamma}$

Based on an approximation utilizing the Taylor series solution for 10^(x) which is:

$10^{x} = {1 + {x\;{\ln(10)}} + \frac{\left( {x\;{\ln(10)}} \right)^{2}}{2!} + \frac{\left( {x\;{\ln(10)}} \right)^{3}}{3!} + \ldots}$ We have as an approximation:

$10^{- \frac{({D_{{ma}\; x} - D_{m\; i\; n}})}{\gamma}} \cong {1 - \frac{D_{{ma}\; x} - D_{m\; i\; n}}{\gamma}}$ Substituting this into the OD equation from before results in:

${OD} = {{{- \gamma} \cdot {{Log}_{10}\left\lbrack {1 + {\frac{D_{m\; i\; n} - D_{m\;{ax}}}{255 \cdot \gamma} \cdot {PixelValue}}} \right\rbrack}} + D_{m\; i\; n}}$

Using the Maclaurin series relationship:

${{Log}\left( {1 + x} \right)} = {{x - {\frac{1}{2}x^{2}} + {\frac{1}{3}x^{3}} - {\frac{1}{4}x^{4}} + {\ldots\mspace{14mu}{for}}\mspace{14mu} - 1} < x \leq 1}$ Optical Density may be rewritten as:

$\left. \Rightarrow{OD} \right. = {{- {\gamma\left\lbrack {{\frac{D_{m\; i\; n} - D_{{ma}\; x}}{\gamma \cdot 255} \cdot {PV}} - {\frac{1}{2}\left( {\frac{D_{m\; i\; n} - D_{{ma}\; x}}{\gamma \cdot 255} \cdot {PV}} \right)^{2}} + {\frac{1}{3}\left( {\frac{D_{m\; i\; n} - D_{{ma}\; x}}{\gamma \cdot 255} \cdot {PV}} \right)^{3}} - \ldots} \right\rbrack}} + D_{m\; i\; n}}$ where PV=Pixel Value.

Because gamma is much bigger than one, only the linear term is meaningful, so we can ignore all but the linear terms. After factoring out gamma, this results in a linear relationship between Optical Density and Pixel Value.

${OD} = {{\left\lbrack {\frac{D_{{ma}\; x} - D_{m\; i\; n}}{255} \cdot {PV}} \right\rbrack + D_{m\; i\; n}} = {> {{Linear}\mspace{14mu}{Density}}}}$ which results in a line similar to that of FIG. 3.

This results in an actual linear brightness, but an image created with a transfer function created with this function would not appear linear to the human eye. The perceived brightness is brightness as perceived by an average human eye. This is because the human eye is better adapted at discerning differences in intensity between two bright shades rather than two dark shades. For the ideal medical image the image must look linear when viewed from the light box using a human eye. Therefore, what is desired is a linear perceived brightness, which requires greater increases in OD as the pixel value increases. Thus the change in OD from pixel value 0 to pixel value 1 is less than the change in OD from pixel value 254 to pixel value 255. Linear perceived brightness can be defined as increasing the change in optical density as pixel values grow darker.

Achieving a perceived linear brightness can be accomplished by changing gamma to 3 in the above equations, and results in a much closer linear perceived brightness. For γ=3, the above expression for optical density may then be rewritten to comprise the following:

${OD} = {{{- 3} \cdot {{Log}_{10}\left\lbrack {1 + {\frac{PixelValue}{255} \cdot \left( {10^{- \frac{({D_{{ma}\; x} - D_{m\; i\; n}})}{3}} - 1} \right)}} \right\rbrack}} + D_{m\; i\; n}}$

This results in an OD vs. Pixel value curve approximately like that of FIG. 4. While this is not a direct linear relationship, it produces more of a predominately linear relationship between Pixel Value and perceived brightness.

The above equations dealt primarily with instances where optical density was calculated using only one component pixel value. However, grayscale images can be created by the printer using combinations of black and colored ink. Adding cyan, magenta, or yellow to black can be used to achieve greater optical densities than using black ink alone. An example of the ink combinations and the resulting black is shown in FIG. 5.

The desired optical density, OD_(T), as a function of Pixel Value as shown in the above equations, may comprise the Optical Density, OD_(K), of the black ink as a function of the Pixel Values of the Black ink in combination with the optical densities, OD_(C), OD_(M), OD_(Y), produced by the other inks as function of their Pixel Values. OD_(T)=OD_(K)+OD_(C,M,Y) Using the earlier expressions for Optical Density equation results in the following equations for the black and cyan, magenta, and yellow ink:

$\begin{matrix} {{OD}_{T} = {{{- \gamma_{T}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \frac{D_{T\;{ma}\; x} - D_{{Tm}\; i\; n}}{\gamma_{T}}} - 1} \right) \cdot \frac{PV}{255}}} \right\rbrack}} + D_{{Tm}\; i\; n}}} & {{Equation}\mspace{14mu} 1} \\ {{OD}_{D} = {{{- \gamma_{K}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \;\frac{D_{K\;{ma}\; x} - D_{K\; m\; i\; n}}{\gamma_{K}}} - 1} \right) \cdot \frac{PV}{255}}} \right\rbrack}} + D_{{Km}\; i\; n}}} & {{Equation}\mspace{14mu} 2} \\ {{OD}_{C,M,Y} = {{{- \gamma_{C,M,Y}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \;\frac{D_{C,M,{Y\;{ma}\; x}} - D_{C,M,{\gamma\; m\; i\; n}}}{\gamma_{C,M,Y}}} - 1} \right) \cdot \frac{PV}{255}}} \right\rbrack}} + D_{C,M,{{Ym}\; i\; n}}}} & {{Equation}\mspace{14mu} 3} \end{matrix}$

If these are substituted into the earlier equation: OD_(T)=OD_(K)+OD_(C,M,Y) and simplifying we derive a transcendental equation for gamma's

$\left\lbrack {1 + {\left( {10^{- \;\frac{D_{{Tm}\;{ax}} - D_{{Tm}\; i\; n}}{\gamma_{T}}} - 1} \right) \cdot \frac{PV}{255}}} \right\rbrack^{\gamma_{T}} = {\left\lbrack {1 + {\left( {10^{- \;\frac{D_{K\mspace{11mu} m\;{ax}} - D_{K\; m\; i\; n}}{\gamma_{K}}} - 1} \right) \cdot \frac{PV}{255}}} \right\rbrack^{\gamma_{K}} \cdot \left\lbrack {1 + {\left( {10^{- \;\frac{D_{C,M,{Y\;{ma}\; x}} - D_{C,M,{{ym}\; i\; n}}}{\gamma_{C,M,Y}}} - 1} \right) \cdot \frac{PV}{255}}} \right\rbrack^{\gamma_{C,M,Y}}}$

These equations are used to determine theoretical optical density from a given input pixel value based on a desired or observed maximum and minimum optical density that is specific to a given device and media and based on a desired gamma curve. The goal of a proper transfer function is to match this function as closely as possible. Therefore, this function is the benchmark for determining a good transfer function. One may compare the measured optical density created by their transfer function to the theoretical equations above to determine how closely the transfer function performs.

Determining the Appropriate Transfer Function for Grayscale Images

The theoretical relationship between input pixel values and optical density has been explored. Given a theoretical optical density for any given input pixel value, what is needed now is to map that to an output ink quantity. A transfer function does just this by mapping input pixel values to output ink values. Since each printer and media is different, this transfer function will partly be based upon measured observations.

In one example, to accomplish this, a 21 step calibration wedge is printed by passing to the printer the input pixel values as output ink values without modification. This wedge is then measured using a densitometer to determine the OD of the various printed pixel values. Based on these data points, a relationship function between input pixel values and the output optical density can be determined by interpolation or other known mathematical means.

In one example, the printer used includes an Epson Styles Pro 4900 and the transfer function includes an identity LUT (look up table) created using a RIP (e.g., Wasach, Epson's SDK Halftone). In an example the actual optical densities for the black (K) and the combination of the cyan, magenta, and yellow inks is determined based on the following functions as determined by observation of the 21 step wedge using a densitometer, working backwards to arrive at the functions: OD_(K)=0.0000243*PixelValue²+0.002092*PixelValue+0.170 0<PixelValue<255  Equation 4: OD_(C,M,Y)=0.000022*PixelValue²+0.00426*PixelValue 0<PixelValue<102  Equation 5:

Additionally, the transfer function can be configured to generate the following minimum and maximum densities: @D_(Kmax) PV=255, and @D_(C,M,Ymax) PV=102, as determined by observation.

D_(Tmax) D_(Tmin) D_(Kmax) D_(Kmin) D_(C, M, Ymax) D_(C, M, Ymin) 2.88 .17 2.22 .17 .66 0

These values are based on experimentally derived data.

In an example, the transfer function converts the pixel values such that the image on the transparent recording medium is perceived by an observer as linear when viewed from a back lit box: hence γ_(T)=3. In an example, if γ_(C,M,Y) is set to the arbitrary value 0.5 it is possible to solve for γ_(K) in the transcendental equation above

γ_(T) γ_(K) γ_(C, M, Y) 3 2.8 .5 The following optical densities are calculated from the above theoretical equations:

TABLE 1 OD_(C, M, Y) OD_(K) + PixelValue OD_(T) - Eqn. 1 OD_(K) - Eqn. 2 Eqn. 3 OD_(C, M, Y) 255 2.88 2.22 0.66 2.88 242 2.496 1.984 0.512 2.496 230 2.195 1.781 0.423 2.204 217 1.95 1.608 0.361 1.969 204 1.744 1.457 0.312 1.769 191 1.566 1.322 0.272 1.594 179 1.409 1.2 0.239 1.439 166 1.269 1.09 0.21 1.3 153 1.143 0.989 0.184 1.173 140 1.028 0.895 0.161 1.056 128 0.923 0.809 0.141 0.95 115 0.825 0.728 0.122 0.85 102 0.734 0.652 0.104 0.756 89 0.649 0.58 0.088 0.668 77 0.569 0.513 0.073 0.586 64 0.494 0.449 0.059 0.508 51 0.423 0.388 0.046 0.434 38 0.356 0.33 0.033 0.363 26 0.291 0.275 0.022 0.297 13 0.23 0.222 0.011 0.233 0 0.17 0.17 0 0.17

Represented graphically in FIG. 6, the solid line represents the theoretical OD_(T) and the dots represent the theoretical OD_(K)+OD_(C,M) functions

Now that we have our theoretical data in the form of component optical densities OD_(K), OD_(C,M) the transfer function for converting the Pixel Value to the output values (PV_(K) and PV_(C,M,Y)) for the ink nozzles for OD_(C,M,Y) and OD_(K) can be computed using the tables above as well as the earlier observed transfer functions. Simply plug in the optical densities for OD_(K) and OD_(C,M) for the various pixel values into equations 4 and 5, and solve for PixelValue. For example, at pixel value 255, for Black ink we use equation 4 and input 2.22 for the optical density. Recalling equation 4: OD_(K)=0.0000243*PixelValue²+0.002092*PixelValue+0.170, we have 2.22−0.0000243*PixelValue²+0.002092*PixelValue+0.170. Solving for PixelValue leaves us with approximately 251.

This method can be used to approximate a lookup table such as:

TABLE 2 Input Output Output PV PV_(K) PV_(C, M, Y) 255 251 102 242 234 84 230 220 72 217 206 64 204 193 57 191 181 51 179 170 45 166 159 41 153 148 36 140 137 32 128 127 29 115 117 25 102 106 22 89 96 19 77 85 16 64 74 13 51 62 10 38 50 7 26 36 5 13 20 3 0 0 0

The above table represents the output pixel values of K and CMY based on the input pixel value. For example, if the input pixel value is 102, then the black output is 106 and the CMY output is 22. Using table 1, this produces a predicted optical density of 0.734. As will be seen next, the measured optical density actually produced turns out to be 0.75. Note that while the output values are expressed as combined CMY, the actual pixel values for C and M and Y can be computed by eliminating Y or setting it to a very low value (as it adds little to the optical density) and dividing the remaining CMY pixel amount between C and M. Thus for example, if the CMY is 22, one might set Y to 2, and then C and M each to 10.

Using the above relationships the interaction model resulted in the following reproduction of theoretical OD_(T) compared to measured Optical Density.

TABLE 3 OD_(T) OD_(meas) 2.88 2.89 2.496 2.53 2.195 2.29 1.95 2.05 1.744 1.85 1.566 1.66 1.409 1.48 1.269 1.36 1.143 1.23 1.028 1.1 0.923 0.99 0.825 0.87 0.734 0.75 0.649 0.66 0.569 0.59 0.494 0.51 0.423 0.44 0.356 0.37 0.291 0.31 0.23 0.25 0.17 0.17

As can be appreciated in FIG. 7, the dots indicate measured OD and the line indicates the theoretical OD based on pixel value. While the transfer function is close, it can be appreciated that the middle pixel values diverge somewhat.

CIELAB Color Space and Independent CMY Output for Grayscale Images

In the above examples, the ink output for each color nozzle CMY was more or less evenly distributed. In other examples, the ink for each color nozzle CMY can be independently controlled by the transfer function. Moreover, in an example, the transfer function can be derived from the CIE 1976 or L*,a*,b* color space instead of utilizing the CMYK color model. With the CIELAB color space, the ink can be controlled more precisely to achieve better results in the middle pixel values, as shall be shown later. Three parameters control the CIELAB colorspace: L*, a*, b*.

The CIELAB color space is predominately uniform with L* representing lightness where L*=0 is black and L*=100 is diffuse or white. The parameter a* represents tint (blue/green) and b* represents tone (red/yellow).

L* for a color may be calculated based on the CIE 1976 Tristimulus values, X, Y, Z. X and Z are used in the calculation of a* and b* respectively, whereas Y is used to calculate of L*.

Yn=100 and corresponds to the Y value for a reference whitepoint and is related to Optical Density from the well known formula as follows:

$\left( \frac{Y}{Y_{n}} \right) = 10^{- {OD}}$ Which in turn is related to L* as follows:

$L^{*} = {{{116\left( \frac{Y}{Y_{n}} \right)^{1/3}} - {16\mspace{14mu}{for}\mspace{14mu}{Y/{Yn}}}} < {.008856}}$ else $L^{*} = {903.3\left( \frac{Y}{Y_{n}} \right)}$

Based on empirical measurements of optical density from target patches printed from a linear bit value target for C, M, Y,K, L* may be described relative to Pixel value. These patches were printed on an Epson 4900 Stylus Pro machine and measured with an X-Rite densitometer.

The nature of the response curve between pixel value and optical density may be non-linear and described using one or more equations. These equations, shown below, may be based on empirical response curves which may be obtained in the manner described above with respect to equations 4 and 5. L* may be represented for the region between Pixel Values (PV) 0 and 166 as:

$\begin{matrix} {L^{*} = {{\frac{\left( {L_{{ma}\; x}^{*} - L_{m\; i\; n}^{*}} \right)}{{\mathbb{e}}^{{- 1}/\sigma}}\left( \frac{255 - {PV}}{255} \right)^{\beta}{\mathbb{e}}^{- \frac{{(\frac{255 - {PV}}{255})}\alpha}{\sigma}}} + L_{m\; i\; n}^{*}}} & {{Equation}\mspace{14mu} 6} \end{matrix}$

In the equation above, σ and β represent parameters to optimize fit and are based on empirical observation as recorded in the table below. The parameters were determined by plotting measured data and the model on the same graph and adjusting the model parameters until a satisfactory fit was achieved. These parameters are media and ink specific and if the media or ink changes characteristics then the model needs to be adjusted. Separate fit values may be calculated for each of K, C, M, and Y.

0 < PV < 166 K C M Y β 1.5 1.7 1.8 1.5 σ 2.6 2.6 2.6 2.6 α 3.2 2.2 3.3 .7 L*_(max) 85.65 85.65 85.65 85.65 L*_(min) 5.36 47.46 36.97 69.62

For pixel values (PV) above 166, L* may be represented as follows:

Equation 7 $L^{*} = {{\left( {L_{\max}^{*} - L_{\min}^{*}} \right)\left( \frac{255 - {PV}}{255} \right)^{\beta}} + L_{\min}^{*}}$ K C M Y β 1.2 1.35 1.45 1.25 L*_(max) 85.65 85.65 85.65 85.65 L*_(min) 5.36 47.46 36.97 69.62

Again, the fit values for Black, Cyan, Magenta and Yellow ink have been derived empirically. From the L* values above one can calculate the theoretical Optical Density from the following relationship: L*=116(10^(−OD))^(1/3)−16 for OD<2.053 else L*=903.3(10^(−OD))  Equation 8:

FIGS. 8-11 show the OD predicted by the empirically derived model vs measured optical density for the individual colors and black. The dots represent modeled values, and the line represents actual measured values.

The method above describes one of one or more method(s) for controlling both the ratios of K to C,M,Y and C,M,Y to (C+M+Y) and adjusting γ's for C,M,Y.

Below are 3 examples of different ink combinations which will achieve reasonably similar Optical Density curves.

EXAMPLE 1

K C M Y Total C/C + M + Y 48% Dmax 2.226 .3 .3 .025 2.851 M/C + M + Y 48% Dmin .17 0 0 0 .17 Y/C + M + Y  4% γ 1.9 1 1 1 C + M + Y/K 28% To calculate the LUT, first the desired optical density values for the LUT are calculated by using equation 2 and 3 for the various input pixel values. Next, using equation 8, L* is calculated. The value of L* is then fed into either equations 6 and 7 and pixel value is solved for. Example 1 is shown in FIG. 13. The resulting LUT in one example may be:

Input Output Output Output Output PV PV_(K) PV_(C) PV_(M) PV_(Y) 255 255 121 97 26 242 226 113 92 25 230 204 107 88 23 217 184 100 84 22 204 170 94 79 21 191 157 88 75 20 179 146 82 71 17 166 136 77 66 16 153 126 71 61 15 140 116 65 57 14 128 107 59 53 12 115 98 53 48 11 102 89 48 44 10 89 81 42 39 9 77 72 36 35 7 64 63 31 30 6 51 54 25 26 5 38 44 19 20 4 26 33 13 14 2 13 20 7 7 1 0 0 0 0 0

EXAMPLE 2

K C M Y C/C + M + Y 48.50% Dmax 2.226 .4 .4 .025 3.051 M/C + M + Y 48.50% Dmin .17 0 0 0 .17 Y/C + M + Y    3% γ −1.75 1 1 1 C + M + Y/K   37% Example 2 is shown in FIG. 14. As in example 1, the values for the LUT are first calculated by using equation 2, and 3 to calculate optical density for the various input pixel values. Next, using equation 8, L* is calculated. That value is then fed into equation 6 or 7 and pixel value is solved for. The resulting LUT in one example may be:

Input Output Output Output Output PV PV_(K) PV_(C) PV_(M) PV_(Y) 255 255 152 120 26 242 223 142 113 25 230 198 133 106 23 217 178 124 100 22 204 164 116 94 21 191 152 107 88 20 179 140 100 83 17 166 130 92 78 16 153 120 85 73 15 140 111 78 67 14 128 102 71 62 12 115 93 64 56 11 102 85 57 51 10 89 76 50 46 9 77 68 43 40 7 64 59 36 35 6 51 50 30 30 5 38 41 23 24 4 26 31 16 17 2 13 19 8 9 1 0 0 0 0 0

EXAMPLE 3

K C M Y C/C + M + Y 48.80% Dmax 2 .5 .5 .025 3.025 M/C + M + Y 48.80% Dmin .17 0 0 0 .17 Y/C + M + Y  2.40% γ −1.5 1 1 1 C + M + Y/K 51.20% Example 3 is shown in FIG. 15. As before, the values for the LUT are first calculated by using equation 2, and 3 to calculate optical density for the various input pixel values. Next, using equation 8, L* is calculated. That value is then fed into equation 6 or 7 and pixel value is solved for. The resulting LUT in one example may be:

Input Output Output Output Output PV PV_(K) PV_(C) PV_(M) PV_(Y) 255 236 192 141 26 242 206 173 132 25 230 182 157 123 23 217 166 145 115 22 204 153 134 108 21 191 141 125 100 20 179 130 115 93 17 166 121 105 87 16 153 111 96 81 15 140 102 88 75 14 128 94 81 69 12 115 86 73 63 11 102 78 65 57 10 89 71 57 51 9 77 63 49 45 7 64 55 41 38 6 51 47 33 32 5 38 38 26 26 4 26 28 18 19 2 13 17 9 10 1 0 0 0 0 0

The three examples above demonstrate how similar optical density may be achieved using the methodology described herein with different amounts of the one or more ink colors comprising the inks 160. However, the Tint & Tone for the 3 examples are quite different as shown by FIGS. 16 and 17.

Use of Light Inks for Grayscale Images

It is possible to achieve a desired tint and tone as well as linear optical density as described above with 4 inks comprising Black, Cyan, Magenta, and Yellow. In some examples, the use of these inks in areas of low density may result in increased graininess. One method to reduce graininess in areas of low density comprises the use of additional “light” inks which create less density per volume and therefore allow for the printer to lay down more “dots” per inch, thereby reducing graininess, but keeping the optical density the same.

As developed above, total Optical Density, OD_(T) may be described as comprising the sum of the optical density curves of the one or more individual inks 142. OD_(T)=OD_(K)+OD_(C)+OD_(M)+OD_(Y)

This equation may be further expanded to include the use of light inks as well. The Cyan Optical Density above may be thought of as comprising the total optical density of the optical density curves for the one or more Cyan inks which may comprise regular and light Cyan as shown below: OD_(CT)=OD_(C)+OD_(LC)

Similarly, Magenta and Black Optical Density may comprise the Optical Density of the individual inks as shown below: OD_(MT)=OD_(M)+OD_(LM) OD_(KT)=OD_(K)+OD_(LK)+OD_(LLK)

This may result in the following description of total Optical Density. OD_(T)=OD_(KT)+OD_(CT)+OD_(MT)+OD_(Y)

With this new schema a weighting function is introduced to “weight” how much dark ink to light ink to use throughout the Bit Value range.

${WF} = {\mathbb{e}}^{({- \frac{{({1 - \frac{PV}{255}})}^{2}}{Spread}})}$ Where the spread is another fit parameter which determines the “spread” of the Gaussian distribution. OD_(C)=OD_(CT)*WF_(C) and OD_(LC)=OD_(CT)−OD_(C) OD_(M)=OD_(MT)*WF_(M) and OD_(LM)=OD_(MT)−OD_(M) OD_(K)=OD_(KT)*WF_(K) and OD_(LK)=WF_(LK)*(OD_(KT)−OD_(K)) and OD_(LLK)=OD_(KT)−(OD_(K)+OD_(LLK))

As described earlier, a Transformation or LUT's for the Light inks: L* and PV relationship may be derived empirically.

$L^{*} = {{\frac{\left( {L_{\max}^{*} - L_{\min}^{*}} \right)}{e^{{- 1}/\sigma}}\left( \frac{255 - {PV}}{255} \right)^{\beta}e^{\frac{{(\frac{255 - {PV}}{255})}^{\alpha}}{\sigma}}} + L_{\min}^{*}}$ LK LC LM LLK β 1.32 1.3 1.25 1.15 σ 1.55 1.6 1.6 1.6 α 1.26 1.15 1.15 1.11 L*_(max) 85.65 85.65 85.65 85.65 L*_(min) 32.06 59.04 54.86 61.45 For PV>216:

$L^{*} = {{\left( {L_{\max}^{*} - L_{\min}^{*}} \right)\left( \frac{255 - {PV}}{255} \right)^{\beta}} + L_{\min}^{*}}$ LK LC LM LLK β .98 .98 .95 .85 L*_(max) 85.65 85.65 85.65 85.65 L*_(min) 32.06 59.04 54.86 61.45

FIGS. 18-21 illustrate the density model vs. observed optical densities for the light inks.

EXAMPLE 4 Inclusion of Light Inks

KT CT MT Y Total C/C + M + Y 48% Dmax 2.226 .3 .3 .025 2.851 M/C + M + Y 48% Dmin .17 0 0 0 .17 Y/C + M + Y  4% γ −1.9 1 1 1 C + M + Y/K 28% With the following weighted values:

WF_(K) WF_(C) WF_(M) WF_(LK) Spread .125 .1 .1 1 FIG. 22 graphically illustrates the various output ink values per given input bit values for the various color inks. FIG. 23 shows the optical density curve for Example 4. As in example 1, the values for the LUT are first calculated by using equation 2 and 3 to calculate optical density for the various input pixel values. Next, using equation 8, L* is calculated. That value is then fed into equations 6 or 7 and pixel value is solved for. Note that to split the input optical densities for Equations 6 and 7 into the regular and light components, the following formulas may be used: OD_(C)=OD_(CT)*WF_(C) and OD_(LC)=OD_(CT)−OD_(C) OD_(M)=OD_(MT)*WF_(M) and OD_(LM)=OD_(MT)−OD_(M) OD_(K)=OD_(KT)*WF_(K) and OD_(LK)=WF_(LK)*(OD_(KT)−OD_(K)) and OD_(LLK)=OD_(KT)−(OD_(K)+OD_(LLK)) The resulting LUT in one example may be:

Input Output Output Output Output Output Output Output Output PV PV_(K) PV_(LK) PV_(LLK) PV_(C) PV_(LC) PV_(M) PV_(LM) PV_(Y) 255 255 0 0 121 0 97 0 26 242 223 22 0 111 9 91 8 25 230 193 60 2 98 28 82 27 23 217 166 93 8 84 51 72 48 22 204 142 118 20 68 72 60 67 21 191 119 135 35 53 89 48 83 20 179 97 144 53 39 100 37 93 17 166 77 147 70 27 106 27 99 16 153 59 145 86 18 108 19 100 15 140 44 138 98 11 106 12 99 14 128 31 128 107 6 102 7 95 12 115 21 116 112 3 96 4 89 11 102 13 103 114 2 88 2 82 10 89 7 89 112 1 79 1 74 9 77 3 75 108 0 69 0 65 7 64 2 60 100 0 60 0 56 6 51 1 46 89 0 49 0 46 5 38 0 33 75 0 38 0 35 4 26 0 21 57 0 26 0 24 2 13 0 10 33 0 13 0 12 1 0 0 0 0 0 0 0 0 0

EXAMPLE 5 Inclusion of Light Inks

KT CT MT Y Total C/C + M + Y 48.4% Dmax 2.226 .3 .32 0 2.851 M/C + M + Y 51.6% Dmin .17 0 0 0 .17 Y/C + M + Y   0% γ −1.9 1 1 1 C + M + Y/K  27% With the following weighted values:

WF_(K) WF_(C) WF_(M) WF_(LK) Spread .125 .1 .1 1 FIG. 24 graphically illustrates the various output ink values per given input bit values for the various color inks. FIG. 25 shows the optical density curve for Example 5. Again, the actual LUT can be calculated in the same manner as example 4. One example LUT may be:

Input Output Output Output Output Output Output Output Output PV PV_(K) PV_(LK) PV_(LLK) PV_(C) PV_(LC) PV_(M) PV_(LM) PV_(Y) 255 255 0 0 121 0 102 0 0 242 223 22 0 111 9 95 8 0 230 193 60 2 98 28 86 28 0 217 166 93 8 84 51 75 50 0 204 142 119 20 68 72 62 70 0 191 119 135 36 53 89 50 86 0 179 97 145 53 39 100 38 97 0 166 77 148 71 27 106 28 103 0 153 59 145 86 18 108 20 105 0 140 44 139 99 11 106 13 103 0 128 31 129 108 6 102 7 99 0 115 21 117 113 3 96 4 93 0 102 13 104 115 2 88 2 86 0 89 7 90 113 1 79 1 77 0 77 3 75 109 0 69 0 68 0 64 2 61 101 0 60 0 58 0 51 1 47 90 0 49 0 48 0 38 0 34 76 0 38 0 36 0 26 0 21 58 0 26 0 25 0 13 0 10 33 0 13 0 12 0 0 0 0 0 0 0 0 0 0

In Examples 4 & 5 it can be seen that different combinations of ink can produce similar optical density curves but very different Tint & Tone as shown by the a* and b* plots in FIGS. 26 and 27.

Color Imagery with CIELAB Color Space

FIG. 28 shows one example overview of a method of printing color medical imagery. The image that is passed to the printer comprises a (r,g,b) value for each pixel in the image. This (r,g,b) value must be separated into separate grayscale components and color components at operation 28010. These components would be R, G, B, C, M, Y, and K. At operation 28020, a lookup table is applied to each of these components producing a value for each of the print heads (K, LK, LLK, C, LC, M, LM, Y, Or, Gr). The values for each of the print heads for each of the individual components are then combined together at operation 28030 and used to produce the final ink output values for each pixel.

Separating the Color and Grayscale Components

First, we assume that PV=0 is D_(max) and PV=255 is D_(min). The original input pixel may be denoted (r₁,g₁,b₁)

K=largest component of the (r₁,g₁,b₁) pixel. So if the (r₁,g₁,b₁) input value is (255,0,0) then the K component is 255.

Next, we remove the K component from the (r₁,g₁,b₁) pixel to create an (r₂,g₂,b₂) pixel. To do this, we apply: (r ₂ ,g ₂ ,b ₂)=(255,255,255)+(r ₁ ,g ₁ ,b ₁)−MAX(r ₁ ,g ₁ ,b ₁)

For example, if (r₁,g₁,b₁)=(155, 175, 15) then K would be 175 and (r₂,g₂,b₂) would be (410, 430, 270)−MAX(r₁,g₁,b₁)=(235, 255, 95).

Now, we convert the color image to CMY space. C _(t)=1−r ₂/255 M _(t)=1−g ₂/255 Y _(t)=1−b ₂/255

The Red component is defined as: R _(c)=255−255*Min(M _(t) ,Y _(t))

The Green component is defined as: G _(c)=255−255*Min(Y _(t) ,C _(r))

The Blue component is defined as: B _(c)=255−255*Min(C _(t) ,M _(t))

The Cyan component is defined as: C _(c)=255+R ₂−Min(G _(c) ,B _(c))

The Magenta component is defined as: M _(c)=255+G ₂−Min(B _(c) ,R _(c))

The Yellow component is defined as: Y _(c)=255+B ₂−Min(G _(c) ,R _(c))

Transformation or LUT's: L* & BV Relationship for All the Inks Available

Repeating equations 6, 7 from above and determining new fit parameters more suitable for color imagery with the addition of Orange and Green we have: (Bit value or BV is interchangeable with PV or pixel value). FOR 0<BV<166

Equation 9 $L^{*} = {{\frac{\left( {L_{\max}^{*} - L_{\min}^{*}} \right)}{e^{{- 1}/\sigma}}\left( \frac{255 - {BV}}{255} \right)^{\beta}e^{\frac{{(\frac{255 - {BV}}{255})}^{\alpha}}{\sigma}}} + L_{\min}^{*}}$ K C M Y O G β 1.46 1.72 1.81 1.55 1.95 1.35 σ 2.66 2.71 2.62 2.6 3.5 2.25 α 3.0 2.31 3.31 .66 2.5 1.8 L*_(max) 85.65 85.65 85.65 85.65 85.65 85.65 L*_(min) 6.29 48.5 8.6 70.9 50.8 70.12 FOR BV>166

Equation 10 $L^{*} = {{\left( {L_{\max}^{*} - L_{\min}^{*}} \right)\left( \frac{255 - {BV}}{255} \right)^{\beta}} + L_{\min}^{*}}$ K C M Y O G β 1.17 1.33 1.43 1.28 1.65 1 L*_(max) 85.65 85.65 85.65 85.65 85.65 85.65 L*_(min) 6.29 48.5 8.6 70.9 50.8 70.12

Transformation or LUTs for the Light Inks: L* & BV Relationship

Note that BV and PV are the same (bit value and pixel value) and may be used interchangeably. FOR 0<BV<216

Equation 11 $L^{*} = {{\frac{\left( {L_{\max}^{*} - L_{\min}^{*}} \right)}{e^{{- 1}/\sigma}}\left( \frac{255 - {BV}}{255} \right)^{\beta}e^{\frac{{(\frac{255 - {BV}}{255})}^{\alpha}}{\sigma}}} + L_{\min}^{*}}$ LK LC LM LLK β 1.32 1.37 1.3 1.19 σ 1.56 1.6 1.6 1.6 α 1.23 1.15 1.15 1.11 L*_(max) 85.65 85.65 85.65 85.65 L*_(min) 34.32 61.4 58.1 63.5 FOR BV>216

Equation 12 $L^{*} = {{\left( {L_{\max}^{*} - L_{\min}^{*}} \right)\left( \frac{255 - {BV}}{255} \right)^{\beta}} + L_{\min}^{*}}$ LK LC LM LLK β .93 1.02 .95 .87 L*_(min) 85.65 85.65 85.65 85.65 L*_(min) 34.32 61.4 58.1 63.5

The optical density equation from the grayscale printing is used to provide the optical density targets as a function of bit value and minimum and maximum densities for the various components. The minimum and maximum densities may be determined by printing color patches and using a densometer to measure the actual maximum and minimums. Based on these measurements on an Epson 4900 we have:

Equation 13 ${OD} = {{{- \gamma} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{\frac{D_{\max} - D_{\min}}{\gamma}} - 1} \right) \cdot \frac{BV}{255}}} \right\rbrack}} + D_{\min}}$ R G B C M Y γ 100 100 100 100 100 100 D_(max) 1.377 .547 1.177 .452 .977 .322 D_(min) .177 .177 .177 .177 .177 .177

Just like it was done with Grayscale, one must choose appropriate Optical Density's for each Color component and determine the shape (i.e. gamma curve) as a function of output. For Color preference this becomes a* b* along with L* and controlling both the ratios of color combinations. For example, one uses formula 13 (above) to arrive at a targeted optical density output for various input pixel bit values for each component. Next, using equation 8 L* is solved for using the arrived at optical density. The value of L* is then plugged into the appropriate L* function from equations 9-12 to solve for pixel value given the input L*. This process may be repeated for all input pixel values in the LUT to arrive at the LUT.

Red

The red component is primarily composed of magenta and some light magenta, yellow and orange.

The ratios of the various ink colors and the Optical Density max and min of Light magenta (as converted from L* from the table earlier) is shown below:

R_(O) + R_(Y)/R_(M) .28 R_(Y)/R_(O) .15 LI Threshold .24 γ_(M) 4.5 Dmax Lm .584 OD Dmin Lm .177 OD Where:

${OD}_{M} = {{{- \gamma_{M}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \frac{D_{R\;{ma}\; x} - D_{R\; m\; i\; n}}{\gamma_{M}}} - 1} \right) \cdot \frac{BV}{255}}} \right\rbrack}} + D_{{Rm}\; i\; n}}$ M=(1−R _(O) +R _(Y) /R _(M))*OD_(M)−OD_(Lm)

-   Lm=If     -   (1−R_(O)+R_(Y)/R_(M))*OD_(M)/(Dmax Lm−Dmin Lm)<LI Threshold then         (1−R_(O)+R_(Y)/R_(M))*OD_(M)     -   Else     -   LI*(Dmax Lm−Dmin Lm)         Y=R _(Y) /R _(O)*(OD_(R)−OD_(M)−OD_(Lm))         O=(1−R _(Y) /R _(O))*(OD_(R)−OD_(M)−OD_(Lm))

Using the processes described above to calculate the grayscale LUT, a LUT may be derived similar to:

Input PV M LM Y O 0 24 166 201 146 13 47 166 202 147 26 66 166 202 148 38 83 166 203 150 51 98 166 205 152 64 110 166 206 155 77 121 166 208 158 89 132 166 211 162 102 142 166 213 166 115 152 166 216 171 128 163 166 218 176 140 173 166 221 181 153 183 166 225 187 166 194 166 228 193 179 206 166 231 199 191 218 166 235 206 204 233 166 239 214 217 253 166 243 224 230 255 190 247 234 242 255 219 251 244 255 255 255 255 255

Green

The Green component is mostly Cyan, with some light cyan, yellow and green.

G_(g) + G_(Y)/G_(C) .89 G_(Y)/G_(g) .36 LI Threshold 1 γ_(C) 100 Dmax Lc .527 Dmin Lc .177

${OD}_{C} = {{{- \gamma_{C}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \frac{D_{{Gm}\;{ax}} - D_{G\; m\; i\; n}}{\gamma_{C}}} - 1} \right) \cdot \frac{BV}{255}}} \right\rbrack}} + D_{{Gm}\; i\; n}}$ C=(1−G _(g) +G _(Y) /G _(C))*OD_(C)−OD_(Lc)

-   Lc=If     -   (1−G_(g)+G_(Y)/G_(C))*OD_(C)/(Dmax Lc−Dmin Lc)<LI     -   (1−G_(G)+G_(Y)/G_(C))*OD_(C)     -   Else     -   LI*(Dmax Lc−Dmin Lc)         Y=G _(Y) /G _(g)*(OD_(G)−OD_(C)−OD_(Lc))         G=(1−G _(Y) /G _(g))*(OD_(G)−OD_(C)−OD_(Lc))

Using the processes described above to calculate the grayscale LUT, a LUT may be derived similar to:

Input C LC Y G 0 255 210 126 0 13 255 212 133 12 26 255 214 139 24 38 255 216 146 37 51 255 218 153 50 64 255 220 159 60 77 255 222 166 69 89 255 225 172 79 102 255 227 179 90 115 255 229 185 100 128 255 231 192 112 140 255 233 198 125 153 255 236 205 135 166 255 238 211 143 179 255 240 217 154 191 255 243 224 169 204 255 245 230 186 217 255 247 236 201 230 255 250 242 221 242 255 252 249 239 255 255 255 255 255

Blue Components

Blue is composed of mainly Cyan with light cyan, magenta, and light magenta.

-   B-Component→C,Lc,M,Lm Lut

B_(M)/B_(C) .55 LI_(C) Threshold .45 LI_(M) Threshold .35 γ_(C) 3.8 Dmax Lc .527 Dmin Lc .177 Dmax Lm .584 Dmin Lm .177 Where

${OD}_{C} = {{{- \gamma_{C}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \frac{D_{B\;{ma}\; x} - D_{{Bm}\; i\; n}}{\gamma_{C}}} - 1} \right) \cdot \frac{BV}{255}}} \right\rbrack}} + D_{B\; m\; i\; n}}$ C=(1−B _(M) /B _(C))*OD_(C)−OD_(Lc)

-   Lc=If     -   (1−B_(M)/B_(C))*OD_(C)/(Dmax Lc−Dmin Lc)<LI_(C)     -   (1−B_(M)/B_(C))*OD_(C)     -   Else     -   LI_(C)*(Dmax Lc−Dmin Lc)         M=OD_(B)−OD_(C)−OD_(Lc)−OD_(Lm) -   Lm=If     -   (OD_(B)−OD_(C)−OD_(Lc))/(Dmax Lm−Dmin Lm)<LI_(m)     -   (OD_(B)−OD_(C)−OD_(Lc))     -   Else     -   LI_(M)*(Dmax Lm−Dmin Lm)

Using the processes described above to calculate the grayscale LUT, a LUT may be derived similar to:

Input C Lc M Lm 0 136 121 130 137 13 146 121 135 137 26 156 121 140 137 38 166 121 145 137 51 175 121 150 137 64 185 121 156 137 77 195 121 162 137 89 204 121 168 137 102 215 121 175 137 115 225 121 182 137 128 236 121 190 137 140 248 121 198 137 153 255 127 207 137 166 255 140 217 137 179 255 154 229 137 191 255 168 245 137 204 255 183 255 146 217 255 199 255 167 230 255 216 255 191 242 255 235 255 219 255 255 255 255 255

Cyan Component

The cyan component is mostly Cyan, with light cyan and yellow components

-   C-Component→C,Lc,Y Lut

C_(Y)/C_(c) .7 LI Threshold .35 γ_(C) 100 Dmax Lc .527 Dmin Lc .177 Where

${OD}_{c} = {{{- \gamma_{c}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \frac{D_{C\; m\;{ax}} - D_{{Cm}\; i\; n}}{\gamma_{c}}} - 1} \right) \cdot \frac{BV}{255}}} \right\rbrack}} + D_{{Cm}\; i\; n}}$ C=(1−C _(Y) /C _(c))*OD_(c)−OD_(Lc)

-   Lc=If     -   (1−C_(Y)/C_(c))*OD_(c)/(Dmax Lc−Dmin Lc)<LI     -   (1−C_(Y)/C_(c))*OD_(c)     -   Else     -   LI*(Dmax Lc−Dmin Lc)         Y=C _(Y) /C _(c)*(OD_(C)−OD_(c)−OD_(Lc))

Using the processes described above to calculate the grayscale LUT, a LUT may be derived similar to:

Input C Lc Y 0 192 144 234 13 197 144 236 26 202 144 237 38 208 144 238 51 213 144 239 64 219 144 240 77 225 144 241 89 231 144 242 102 238 144 243 115 245 144 244 128 252 144 245 140 255 150 246 153 255 159 247 166 255 169 248 179 255 179 249 191 255 189 250 204 255 201 251 217 255 213 252 230 255 226 253 242 255 240 254 255 255 255 255

Magenta

The magenta component is mostly magenta with light magenta, cyan, and light cyan components.

-   M-Component→M,Lm,C,Lc Lut

M_(M)/M_(C) .82 LI_(M) Threshold .2 LI_(C) Threshold 1 γ_(m) 100 Dmax Lc .527 Dmin Lc .177 Dmax Lm .584 Dmin Lm .177 Where

${OD}_{m} = {{{- \gamma_{m}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \frac{D_{M\; m\;{ax}} - D_{{MBm}\; i\; n}}{\gamma_{m}}} - 1} \right) \cdot \frac{BV}{255}}} \right\rbrack}} + D_{M\; m\; i\; n}}$ C→(1−M _(M) /M _(C))*OD_(m)−OD_(Lm)

-   Lc=If     -   (1−M_(M)/M_(C))*OD_(m)/(Dmax Lm−Dmin Lm)<LI_(C)     -   (1−M_(M)/M_(C))*OD_(m)     -   Else     -   LI_(C)*(Dmax Lc−Dmin Lc)         M=OD_(C)−OD_(c)−OD_(Lc)−OD_(Lm) -   Lm=If     -   (OD_(C)−OD_(c)−OD_(Lc))/(Dmax Lm−Dmin Lm)<LI_(m)     -   (OD_(C)−OD_(c)−OD_(Lc))     -   Else     -   LI_(M)*(Dmax Lm−Dmin Lm)

Using the processes described above to calculate the grayscale LUT, a LUT may be derived similar to:

Input M Lm C Lc 0 90 178 255 130 13 99 178 255 135 26 107 178 255 140 38 115 178 255 145 51 122 178 255 150 64 130 178 255 155 77 137 178 255 160 89 145 178 255 166 102 153 178 255 171 115 160 178 255 177 128 168 178 255 183 140 177 178 255 189 153 185 178 255 195 166 195 178 255 201 179 205 178 255 208 191 216 178 255 215 204 228 178 255 222 217 244 178 255 230 230 255 191 255 238 242 255 219 255 246 255 255 255 255 255

Yellow Component

The yellow component is mostly yellow with possibly some light magenta, and light cyan components. However, in this example, no light magenta and light cyan were used.

-   Y-Component→Y,Lm Lc Lut

Y_(Lc) + Y_(Lm)/Y_(y) 0 γ y 100 Where

${OD}_{y} = {{{- \gamma_{y}} \cdot {{Log}_{10}\left\lbrack {1 + {\left( {10^{- \frac{D_{{Ym}\;{ax}} - D_{Ymin}}{\gamma_{y}}} - 1} \right) \cdot \frac{BV}{255}}} \right\rbrack}} + D_{Ymin}}$

-   Y→OD_(y) -   Lc→0 -   Lm→0

Using the processes described above to calculate the grayscale LUT, a LUT may be derived similar to:

Input Y Lc Lm 0 96 255 255 13 103 255 255 26 112 255 255 38 121 255 255 51 129 255 255 64 137 255 255 77 145 255 255 89 153 255 255 102 161 255 255 115 169 255 255 128 177 255 255 140 185 255 255 153 193 255 255 166 201 255 255 179 209 255 255 191 216 255 255 204 224 255 255 217 232 255 255 230 240 255 255 242 247 255 255 255 255 255 255

Combining the Individual Ink Values

Once the ink values for the various individual components are calculated, they are then combined into a final ink value based upon the formulas: K=Grayscale K Light K=Grayscale LK LLK=Grayscale LLK C→Grayscale_(—) C+255−(G _(—) C+255−(M _(—) C+255−(B _(—) C+(255−C _(—) C)))) Lc→Grayscale_(—) Lc+255−(G _(—) Lc+255−(B _(—) Lc+255−(C _(—) Lc+255−(M _(—) Lc+(255−Y _(—) Lc))) M→Grayscale_(—) M+255−(R _(—) M+255−(B _(—) M+(255−M _(—) M))) Lm→Grayscale_(—) Lm+255−(R _(—) Lm+255−(B _(—) Lm+(255−M _(—) Lm))) Y→Y _(—) Y+255−(C _(—) Y+255−(G _(—) Y+(255−R _(—) Y))) O→R _(—) O G→G _(—) G

Results

Theoretical values in sRGB space with Film reference White

sRGB Red Green Blue Film Cyan Magenta Yellow x 0.64 0.3 0.15 0.315 0.224 0.321 0.419 y 0.33 0.6 0.06 0.343 0.328 0.154 0.505 z 0.03 0.1 0.79 0.342 0.448 0.525 0.076

Measured Values

G R B x y x y x y 0.31034 0.60197 0.62906 0.32043 0.16514 0.11589 0.31132 0.59032 0.61939 0.3225 0.17528 0.12956 0.313 0.577 0.60817 0.32501 0.18571 0.14289 0.31462 0.55955 0.59385 0.32752 0.19661 0.15632 0.31696 0.54462 0.57952 0.32868 0.20819 0.1724 0.3173 0.5289 0.56397 0.33032 0.21851 0.18643 0.31855 0.51077 0.54019 0.33291 0.22995 0.20131 0.31843 0.4933 0.51991 0.33534 0.23986 0.21537 0.31839 0.47339 0.49647 0.33535 0.24956 0.22958 0.31925 0.45058 0.47033 0.33709 0.25927 0.24484 0.31705 0.4315 0.44653 0.33804 0.26976 0.26028 0.31509 0.41348 0.42442 0.33816 0.28197 0.2754 0.31495 0.39354 0.39907 0.33885 0.29199 0.29262 0.31418 0.37603 0.37464 0.33942 0.29929 0.3077 0.3142 0.36209 0.3504 0.34075 0.30332 0.32011 0.31516 0.35087 0.32928 0.34103 0.30998 0.3323 0.31513 0.34283 0.31541 0.34324 0.31539 0.34321 Y M C x y x y x y 0.44232 0.51565 0.32404 0.17849 0.21765 0.32677 0.43957 0.51434 0.32443 0.1865 0.22333 0.32746 0.43401 0.5087 0.32404 0.19517 0.22925 0.32936 0.42813 0.50244 0.32378 0.20413 0.23524 0.32958 0.42455 0.49956 0.3242 0.2154 0.24292 0.33175 0.41741 0.49119 0.32328 0.22507 0.2483 0.33333 0.4072 0.47788 0.32202 0.23719 0.25374 0.33494 0.39793 0.46635 0.32218 0.24819 0.25915 0.33564 0.39007 0.45608 0.32289 0.25922 0.26455 0.33693 0.37826 0.43918 0.32126 0.27059 0.27139 0.33824 0.36861 0.42552 0.32116 0.28075 0.27897 0.33901 0.35969 0.41253 0.31958 0.29202 0.28532 0.3387 0.34848 0.39539 0.31791 0.30263 0.29193 0.3397 0.33935 0.38123 0.31652 0.31197 0.29888 0.34097 0.33077 0.36784 0.31476 0.32261 0.30506 0.34205 0.32232 0.35447 0.31463 0.33372 0.31028 0.34245 0.3151 0.34298 0.31555 0.34344 0.31513 0.34312

FIG. 29 shows the results. FIG. 29 shows the color values in sRGB space and the dashed is the inkjet results. The triangle represents the color Gamut defined by the primary values. The goal is to have the dotted lines match the solid lines as closely as possible. 29010 shows how closely blue matches the sRGB space, 29020 shows magenta, 29030 shows red, 29040 shows yellow, 29050 shows green, and 29060 shows cyan.

Density and Densitometers

The density of an output media comprising a transparent base is defined as the common logarithm of the inverse of the transmittance (T).

The transmittance is defined as the portion of incident light impinging on one side of the film which passes through the film. Known densitometers typically comprise a light emitting subsystem with a light source for impinging light on a selected area of the film. A light detecting subsystem which can compromise a photosensitive element such as a photodiode, is positioned on the other side of the film and detects the portion of light passing through the output media.

In an example, an operator can initiate a quality check for the system 100 which might comprise causing the printer 102 to print one or more of test patches of a known value. These test patches can then be read by an internal densitometer 120 and the densitometry data 125 compared with expected optical density. This can comprise a portion of an acceptability method which comprises evaluating the how closely the printed medium reproduced the desired optical density.

In an example, the patches might be printed on the majority of the recording medium 115. In addition to allowing the evaluation of the individual recording medium 115 the accumulated data might be used to detect trends in the overall performance.

The ink-jet printer 102 might automatically print one or more test patches when a change is detected. These changes might comprise, among other things, wear within the ink-jet printer 102, information on the recording medium 114 type or batch, information regarding the inks 142, or environmental data. If the patch's densities are not acceptable there can be a variety of results comprising, among others, notification to the user that the print quality is unacceptable, notification of the need to recalibrate the system, and the like.

In an example, many ink-jet printers 102 will come with default models for transforming the data the resultant recording medium 114 will more closely resemble the desired color/tint and tone if the model is tuned to the particular set of ink(s) 142, recording medium 114 and any additional information which might affect the output image including, but not limited to environmental data such as temperature and humidity.

In an example, the pixel values 136 can correspond to a series of one or more patches of varying densities. After the pixel values 136 have been printed, the one or more patches on the recording medium 114 can be read using a densitometer 132 (reflective or transmittance as appropriate for the recording medium 114 support.) The densitometry data 132 can then be conveyed to the image processing module 104. The method of transfer can include any of the following or combinations of the following: entering the data into the inkjet printer 102 through a simple user interface, transmitting to the image processing module 104 via either a proprietary or standardized wired or wireless system, transmitting via a local area network (LAN) or a wide area network (WAN.) It can be physically transferred to the printer through the use of a physical media which can comprise a CD, DVD or flash memory. The densitometry data 134 can then be used, in conjunction with information on the corresponding Pixel Values to develop an output model to that desired.

In an example, the densitometer 132 described above can be incorporated directly into the ink jet printer 102. This creates the opportunity to more rapidly or more frequently update the densitometry data 134 and the resulting model(s) that can comprise the image processing.

In an example, the Pixel Values to print for the different patches can be stored in the ink-jet printer 102 rather than being input as pixel values 136. This may rely less on the metadata stored with the print.

In an example, the calibration process can be used at a variety of times including, but not limited to: upon the start of a new media, or media lot, upon the replenishment or replacement of ink (s), or upon user request.

Example Machine Implementation

FIG. 12 shows a diagrammatic representation of a machine in the example form of a computer system 30000 within which a set of instructions for causing the machine to perform any one or more of the methods, processes, operations, or methodologies discussed herein may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a Personal Computer (PC), a tablet PC, a Set-Top Box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a Web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Example embodiments can also be practiced in distributed system environments where local and remote computer systems which that are linked (e.g., either by hardwired, wireless, or a combination of hardwired and wireless connections) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory-storage devices (see below).

The example computer system 30000 includes a processor 30002 (e.g., a Central Processing Unit (CPU), a Graphics Processing Unit (GPU) or both), a main memory 30001 and a static memory 30006, which communicate with each other via a bus 30008. The computer system 30000 may further include a video display unit 30010 (e.g., a Liquid Crystal Display (LCD) or a Cathode Ray Tube (CRT)). The computer system 30000 also includes an alphanumeric input device 30012 (e.g., a keyboard), a User Interface (UI) cursor controller 30014 (e.g., a mouse), a disk drive unit 30016, a signal generation device 30018 (e.g., a speaker) and a network interface device 30020 (e.g., a transmitter).

The disk drive unit 30016 includes a machine-readable medium 30022 on which is stored one or more sets of instructions 30024 and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions illustrated herein. The software may also reside, completely or at least partially, within the main memory 30001 and/or within the processor 30002 during execution thereof by the computer system 30000, the main memory 30001 and the processor 30002 also constituting machine-readable media.

The instructions 30024 may further be transmitted or received over a network 30026 via the network interface device 30020 using any one of a number of well-known transfer protocols (e.g., HTTP, Session Initiation Protocol (SIP)).

The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that cause the machine to perform any of the one or more of the methodologies illustrated herein. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic medium.

Method embodiments illustrated herein may be computer-implemented. Some embodiments may include computer-readable media encoded with a computer program (e.g., software), which includes instructions operable to cause an electronic device to perform methods of various embodiments. A software implementation (or computer-implemented method) may include microcode, assembly language code, or a higher-level language code, which further may include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, the code may be tangibly stored on one or more volatile or non-volatile computer-readable media during execution or at other times. These computer-readable media may include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, Random Access Memories (RAMs), Read Only Memories (ROMs), and the like.

Additional Notes

The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention can be practiced. These embodiments are also referred to herein as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

All publications, patents, and patent documents referred to in this document are incorporated by reference herein in their entirety, as though individually incorporated by reference. In the event of inconsistent usages between this document and those documents so incorporated by reference, the usage in the incorporated reference(s) should be considered supplementary to that of this document; for irreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to comply with 37 C.F.R. §1.72(b), to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. 

The invention claimed is:
 1. A method for printing on medium using an ink-jet printer having a plurality of print heads, comprising: accessing an image having a plurality of pixels, each pixel having an r,g,b value; for each pixel, separating each r,g,b value into separate grayscale components and color components; applying a lookup table to each grayscale and color component to generate an ink value for each of the print heads; combining the ink values to produce a final ink output value for each pixel; and employing the final ink output values for printing on medium using the ink-jet printer, wherein the ink-jet printer includes 10 print heads (K,Light K,LLK,C,Lc,M,Lm,Y,O,G), the grayscale and color components are R,G,B,C,M,Y,K, and the final ink output value for each pixel for each of the print heads is determined by the formulas: K=Grayscale K Light K=Grayscale LK LLK=Grayscale LLK C→Grayscale_(—) C+255−(G _(—) C+255−(M _(—) C+255−(B _(—) C+(255−C _(—) C)))) Lc→Grayscale_(—) Lc+255−(G _(—) Lc+255−(B _(—) Lc+255−(C _(—) Lc+255−(M _(—) Lc+(255−Y _(—) Lc))) M→Grayscale_(—) M+255−(R _(—) M+255−(B _(—) M+(255−M _(—) M))) Lm→Grayscale_(—) Lm+255−(R _(—) Lm+255−(B _(—) Lm+(255−M _(—) Lm))) Y→Y _(—) Y+255−(C _(—) Y+255−(G _(—) Y+(255−R _(—) Y))) O→R _(—) O G→G _(—) G. 